Damage Detection Using Two-Stage Compressive Sensing

NASA is developing new vehicles for human space flight. Many of these spacecraft are targeted for long-term use, which offers challenges for inspection and maintenance. In orbit or on the Moon or Mars, the use of traditional NDE is prohibitive because of location and inaccessibility, and infrequent inspection can lead to conservative, high-weight designs. NASA is seeking technologies to facilitate inspections on large complex structures and provide reliable assessments of structural health.nbsp;Structural health monitoring (SHM) can help overcome inspection difficulties and has shown good results on small structures. However, transition to large complex structures has been slow. Some reasons for the slow adoption are difficulties with large sensor arrays, timely analysis of large data sets, and overall weight of the system. In order to realize the benefits of SHM, therersquo;s a need to reduce the number of sensors and minimize data acquisition processes while maintaining the ability to accurately detect, locate, and characterize damage.nbsp;Compressive Sensing (CS) has been shown to greatly reduce data acquisition/processing burdens by providing accurate signal recovery from far fewer samples than conventionally needed. In this project, it is proposed to develop data analysis software and hardware to detect damage in large complex structures using CS at two stages in the data acquisition/analysis process: (1) temporally undersampled sensor signals from (2) spatially undersampled sensor arrays, resulting in faster data acquisition and reduced data sets without any loss in damage detection ability. The overarching goal is to reduce data acquisition requirements (energy consumption, number of sensors, data collection and storage, and total system weight) of NDE/SHM systems without compromising damage detection accuracy or probability of detection.